Overview

Dataset statistics

Number of variables11
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.1 KiB
Average record size in memory88.1 B

Variable types

Numeric10
Categorical1

Warnings

target is uniformly distributed Uniform
X0 has unique values Unique
X1 has unique values Unique
X2 has unique values Unique
X3 has unique values Unique
X4 has unique values Unique
X5 has unique values Unique
X6 has unique values Unique
X7 has unique values Unique
X8 has unique values Unique
X9 has unique values Unique

Reproduction

Analysis started2021-02-12 16:48:22.889540
Analysis finished2021-02-12 17:02:44.765901
Duration14 minutes and 21.88 seconds
Software versionpandas-profiling v2.10.1
Download configurationconfig.yaml

Variables

X0
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.07936307449
Minimum-4.261079404
Maximum2.693764964
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:02:54.864544image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-4.261079404
5-th percentile-1.684615449
Q1-0.7647559476
median-0.09377395839
Q30.6080984489
95-th percentile1.546387133
Maximum2.693764964
Range6.954844368
Interquartile range (IQR)1.372854396

Descriptive statistics

Standard deviation0.9934158816
Coefficient of variation (CV)-12.51735631
Kurtosis-0.1321707479
Mean-0.07936307449
Median Absolute Deviation (MAD)0.6929221946
Skewness-0.1051109589
Sum-79.36307449
Variance0.9868751139
MonotocityNot monotonic
2021-02-12T12:03:03.659711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.4429237891
 
0.1%
0.5549314581
 
0.1%
-0.42158301761
 
0.1%
-1.3433336611
 
0.1%
-0.5257627661
 
0.1%
1.0148717011
 
0.1%
-1.0612862471
 
0.1%
0.70643615231
 
0.1%
0.74724504881
 
0.1%
0.24122251331
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-4.2610794041
0.1%
-3.1106045791
0.1%
-2.9953294451
0.1%
-2.9172779961
0.1%
-2.6800358711
0.1%
-2.5024075381
0.1%
-2.4620733591
0.1%
-2.3345218731
0.1%
-2.3222077581
0.1%
-2.3011308591
0.1%
ValueCountFrequency (%)
2.6937649641
0.1%
2.6731540791
0.1%
2.3769155481
0.1%
2.3180436051
0.1%
2.3128626361
0.1%
2.1617796671
0.1%
2.1408425471
0.1%
2.1351773761
0.1%
2.1325338191
0.1%
2.057978421
0.1%

X1
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01135526892
Minimum-3.403260273
Maximum3.42615888
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:03:11.942333image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.403260273
5-th percentile-1.665266611
Q1-0.6735799494
median0.0390375479
Q30.726667252
95-th percentile1.620252091
Maximum3.42615888
Range6.829419154
Interquartile range (IQR)1.400247201

Descriptive statistics

Standard deviation1.008587535
Coefficient of variation (CV)88.8211052
Kurtosis-0.02882587755
Mean0.01135526892
Median Absolute Deviation (MAD)0.7004703267
Skewness-0.06142536411
Sum11.35526892
Variance1.017248816
MonotocityNot monotonic
2021-02-12T12:03:20.419562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.15903522211
 
0.1%
-0.33011369281
 
0.1%
2.0455836571
 
0.1%
0.80151223881
 
0.1%
1.5433462521
 
0.1%
-0.45993388141
 
0.1%
-1.0904930671
 
0.1%
0.44416764051
 
0.1%
0.50502898911
 
0.1%
-0.24261609851
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.4032602731
0.1%
-3.0965126561
0.1%
-2.9300997271
0.1%
-2.6519100131
0.1%
-2.6454364381
0.1%
-2.6406071851
0.1%
-2.5345459081
0.1%
-2.5342680321
0.1%
-2.4552977631
0.1%
-2.4000103721
0.1%
ValueCountFrequency (%)
3.426158881
0.1%
3.0117732051
0.1%
2.885540641
0.1%
2.8781160091
0.1%
2.7348109081
0.1%
2.4804244621
0.1%
2.4213809051
0.1%
2.3337234251
0.1%
2.255548021
0.1%
2.2408758561
0.1%

X2
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01623513249
Minimum-3.502568632
Maximum3.731083906
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:03:28.947940image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.502568632
5-th percentile-1.623219475
Q1-0.6826880627
median0.06230547445
Q30.7135875792
95-th percentile1.700613955
Maximum3.731083906
Range7.233652538
Interquartile range (IQR)1.396275642

Descriptive statistics

Standard deviation1.042556242
Coefficient of variation (CV)64.21606002
Kurtosis0.1409335626
Mean0.01623513249
Median Absolute Deviation (MAD)0.7008574419
Skewness-0.0727347431
Sum16.23513249
Variance1.086923518
MonotocityNot monotonic
2021-02-12T12:03:38.153235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0240808121
 
0.1%
0.56299677811
 
0.1%
-0.93549914261
 
0.1%
-0.73901407211
 
0.1%
0.056411080911
 
0.1%
1.0568324511
 
0.1%
0.54087483271
 
0.1%
0.48712470811
 
0.1%
-1.7325316231
 
0.1%
0.29478239241
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.5025686321
0.1%
-3.1647996261
0.1%
-3.1634521551
0.1%
-3.088312011
0.1%
-2.9348635681
0.1%
-2.9296255081
0.1%
-2.8977044281
0.1%
-2.8950621081
0.1%
-2.862212941
0.1%
-2.7599523291
0.1%
ValueCountFrequency (%)
3.7310839061
0.1%
3.0654101991
0.1%
2.8780726921
0.1%
2.8042481131
0.1%
2.6107201041
0.1%
2.5749673261
0.1%
2.5611240061
0.1%
2.560228521
0.1%
2.5089578811
0.1%
2.440194411
0.1%

X3
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03135454991
Minimum-3.219314711
Maximum3.275007308
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:03:47.232814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.219314711
5-th percentile-1.560101496
Q1-0.6028206023
median0.03547038857
Q30.6846641037
95-th percentile1.545985014
Maximum3.275007308
Range6.494322019
Interquartile range (IQR)1.287484706

Descriptive statistics

Standard deviation0.9546033192
Coefficient of variation (CV)30.44544802
Kurtosis0.01409442076
Mean0.03135454991
Median Absolute Deviation (MAD)0.6434131273
Skewness0.03051557768
Sum31.35454991
Variance0.911267497
MonotocityNot monotonic
2021-02-12T12:03:55.894127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.3470073771
 
0.1%
0.66777303071
 
0.1%
-0.65959786271
 
0.1%
0.68822049011
 
0.1%
-1.2647984141
 
0.1%
-0.58971191011
 
0.1%
0.2289154071
 
0.1%
0.050406567581
 
0.1%
0.20168709211
 
0.1%
-1.8720624131
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.2193147111
0.1%
-2.6720382641
0.1%
-2.6213434051
0.1%
-2.5651001631
0.1%
-2.5308031291
0.1%
-2.4637268211
0.1%
-2.4574557261
0.1%
-2.3608666371
0.1%
-2.2894727621
0.1%
-2.2615866271
0.1%
ValueCountFrequency (%)
3.2750073081
0.1%
2.8116314551
0.1%
2.7131798021
0.1%
2.6038228091
0.1%
2.559328431
0.1%
2.5196761471
0.1%
2.5029441111
0.1%
2.4615368041
0.1%
2.3772187631
0.1%
2.3470073771
0.1%

X4
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01472360471
Minimum-3.824906078
Maximum3.437078936
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:04:04.852292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.824906078
5-th percentile-1.683510021
Q1-0.6766941785
median0.0614063183
Q30.6900985879
95-th percentile1.627090631
Maximum3.437078936
Range7.261985014
Interquartile range (IQR)1.366792766

Descriptive statistics

Standard deviation1.010130917
Coefficient of variation (CV)68.60622361
Kurtosis0.1178857499
Mean0.01472360471
Median Absolute Deviation (MAD)0.6866409212
Skewness-0.06709951471
Sum14.72360471
Variance1.020364469
MonotocityNot monotonic
2021-02-12T12:04:13.572367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.6011668721
 
0.1%
-0.013475622061
 
0.1%
-2.2005998451
 
0.1%
0.26972270791
 
0.1%
0.13837899061
 
0.1%
0.4289939561
 
0.1%
0.00116140481
 
0.1%
0.59348943191
 
0.1%
-0.34846487891
 
0.1%
-0.51036973241
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.8249060781
0.1%
-3.2105905081
0.1%
-3.1981722131
0.1%
-2.7528566991
0.1%
-2.6745898591
0.1%
-2.4652869271
0.1%
-2.3711162351
0.1%
-2.3632168031
0.1%
-2.3049258941
0.1%
-2.2941407681
0.1%
ValueCountFrequency (%)
3.4370789361
0.1%
3.358733191
0.1%
3.1780123561
0.1%
3.1299688551
0.1%
2.6787633751
0.1%
2.52441131
0.1%
2.3680037031
0.1%
2.3481347391
0.1%
2.3310308421
0.1%
2.2417473411
0.1%

X5
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.007406887774
Minimum-3.014140153
Maximum2.822694582
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:04:22.441451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.014140153
5-th percentile-1.624096994
Q1-0.6967485045
median-0.02731737188
Q30.6991130869
95-th percentile1.683197106
Maximum2.822694582
Range5.836834735
Interquartile range (IQR)1.395861591

Descriptive statistics

Standard deviation1.008446813
Coefficient of variation (CV)-136.1498707
Kurtosis-0.2788736795
Mean-0.007406887774
Median Absolute Deviation (MAD)0.6952887364
Skewness-0.01434402069
Sum-7.406887774
Variance1.016964974
MonotocityNot monotonic
2021-02-12T12:04:31.403453image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.49595402581
 
0.1%
0.080918311611
 
0.1%
-0.86612868121
 
0.1%
-1.1512315051
 
0.1%
0.24800213661
 
0.1%
-0.24221217281
 
0.1%
0.50439476071
 
0.1%
0.90151097371
 
0.1%
1.3243997491
 
0.1%
-0.62831275671
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.0141401531
0.1%
-2.8183280481
0.1%
-2.7035984981
0.1%
-2.6441077351
0.1%
-2.4778461271
0.1%
-2.4475937011
0.1%
-2.3908303251
0.1%
-2.3853943311
0.1%
-2.3666965221
0.1%
-2.3497043781
0.1%
ValueCountFrequency (%)
2.8226945821
0.1%
2.6056480071
0.1%
2.5250000971
0.1%
2.5149936361
0.1%
2.4756792911
0.1%
2.448920721
0.1%
2.425157471
0.1%
2.327226961
0.1%
2.3160526981
0.1%
2.2878444721
0.1%

X6
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.02432578441
Minimum-2.918915809
Maximum2.886437047
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:04:40.362327image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.918915809
5-th percentile-1.646479209
Q1-0.6961758427
median0.0005211377652
Q30.6614114569
95-th percentile1.619701155
Maximum2.886437047
Range5.805352856
Interquartile range (IQR)1.3575873

Descriptive statistics

Standard deviation1.001101792
Coefficient of variation (CV)-41.15393673
Kurtosis-0.1850922467
Mean-0.02432578441
Median Absolute Deviation (MAD)0.6850938153
Skewness-0.008805870273
Sum-24.32578441
Variance1.002204799
MonotocityNot monotonic
2021-02-12T12:04:48.684058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.71181396261
 
0.1%
0.63928146081
 
0.1%
0.85640582411
 
0.1%
-0.50962394391
 
0.1%
0.65145478551
 
0.1%
0.1583193661
 
0.1%
0.48646969781
 
0.1%
0.90358257161
 
0.1%
-0.11445965251
 
0.1%
-1.3648095241
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-2.9189158091
0.1%
-2.8408953091
0.1%
-2.7519813141
0.1%
-2.7022773331
0.1%
-2.6750719341
0.1%
-2.6388476391
0.1%
-2.5406800531
0.1%
-2.4444916931
0.1%
-2.3724638251
0.1%
-2.3686730021
0.1%
ValueCountFrequency (%)
2.8864370471
0.1%
2.7298813051
0.1%
2.6949084521
0.1%
2.6919030551
0.1%
2.5327675371
0.1%
2.4743503281
0.1%
2.4531013881
0.1%
2.4059618781
0.1%
2.403737051
0.1%
2.3275132241
0.1%

X7
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01117125658
Minimum-3.627640928
Maximum2.713242987
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:04:57.063793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.627640928
5-th percentile-1.559044421
Q1-0.6722948247
median0.02640886088
Q30.6680914723
95-th percentile1.644578884
Maximum2.713242987
Range6.340883914
Interquartile range (IQR)1.340386297

Descriptive statistics

Standard deviation0.9831254541
Coefficient of variation (CV)88.00491214
Kurtosis0.1520574108
Mean0.01117125658
Median Absolute Deviation (MAD)0.6719734731
Skewness-0.07666550934
Sum11.17125658
Variance0.9665356585
MonotocityNot monotonic
2021-02-12T12:05:05.178113image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.28254517041
 
0.1%
1.0213934931
 
0.1%
0.74174313791
 
0.1%
-0.74200516511
 
0.1%
-0.88342608551
 
0.1%
-0.092858401671
 
0.1%
1.6464374821
 
0.1%
-1.5380753221
 
0.1%
0.11631986521
 
0.1%
1.2215875681
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.6276409281
0.1%
-3.2313733551
0.1%
-3.0923218031
0.1%
-2.9372049081
0.1%
-2.8453121491
0.1%
-2.6723494351
0.1%
-2.558935041
0.1%
-2.5287381621
0.1%
-2.5030891231
0.1%
-2.415424871
0.1%
ValueCountFrequency (%)
2.7132429871
0.1%
2.6860673221
0.1%
2.5095469121
0.1%
2.4869994761
0.1%
2.4841721061
0.1%
2.4527601451
0.1%
2.4259469881
0.1%
2.4041209941
0.1%
2.4028983451
0.1%
2.3467158461
0.1%

X8
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.01117645131
Minimum-2.690269344
Maximum2.887339446
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:05:12.734567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.690269344
5-th percentile-1.666437295
Q1-0.6549452747
median0.002582986365
Q30.6740076835
95-th percentile1.549960541
Maximum2.887339446
Range5.57760879
Interquartile range (IQR)1.328952958

Descriptive statistics

Standard deviation0.9709027348
Coefficient of variation (CV)-86.8703945
Kurtosis-0.2523234842
Mean-0.01117645131
Median Absolute Deviation (MAD)0.6669878665
Skewness-0.05454493501
Sum-11.17645131
Variance0.9426521205
MonotocityNot monotonic
2021-02-12T12:05:20.941747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.46670563931
 
0.1%
-0.22799994521
 
0.1%
-0.73268215371
 
0.1%
-0.49323667351
 
0.1%
0.43450168141
 
0.1%
1.4270423821
 
0.1%
-0.63915900961
 
0.1%
0.29186638851
 
0.1%
0.0027834895481
 
0.1%
-0.56672794481
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-2.6902693441
0.1%
-2.6408476111
0.1%
-2.597398571
0.1%
-2.5415660311
0.1%
-2.3660203061
0.1%
-2.278410291
0.1%
-2.2758753121
0.1%
-2.2688784981
0.1%
-2.2420435411
0.1%
-2.240252551
0.1%
ValueCountFrequency (%)
2.8873394461
0.1%
2.5735794651
0.1%
2.5422958441
0.1%
2.5150164641
0.1%
2.4852487221
0.1%
2.362755611
0.1%
2.2909757951
0.1%
2.2470259591
0.1%
2.2362113641
0.1%
2.2180669131
0.1%

X9
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03512356384
Minimum-3.479559419
Maximum3.455788454
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:05:29.342787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.479559419
5-th percentile-1.602754355
Q1-0.6369721368
median0.05282916448
Q30.6943219573
95-th percentile1.642346911
Maximum3.455788454
Range6.935347873
Interquartile range (IQR)1.331294094

Descriptive statistics

Standard deviation0.9910726355
Coefficient of variation (CV)28.21674475
Kurtosis-0.09929613344
Mean0.03512356384
Median Absolute Deviation (MAD)0.6604875069
Skewness0.02049595034
Sum35.12356384
Variance0.9822249689
MonotocityNot monotonic
2021-02-12T12:05:38.861598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.4424298561
 
0.1%
-0.70081508241
 
0.1%
-0.3456814221
 
0.1%
-1.1971095461
 
0.1%
-0.26522524431
 
0.1%
0.24974947171
 
0.1%
0.46022702971
 
0.1%
1.9190017861
 
0.1%
-0.25251958481
 
0.1%
1.2443535831
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.4795594191
0.1%
-2.8613083081
0.1%
-2.8098026681
0.1%
-2.393392291
0.1%
-2.214346511
0.1%
-2.2132888181
0.1%
-2.2122588391
0.1%
-2.185700051
0.1%
-2.1796804031
0.1%
-2.160483681
0.1%
ValueCountFrequency (%)
3.4557884541
0.1%
2.7578195531
0.1%
2.7173145511
0.1%
2.5943619111
0.1%
2.4424298561
0.1%
2.4189169951
0.1%
2.339718081
0.1%
2.3005731
0.1%
2.2837242771
0.1%
2.275323961
0.1%

target
Categorical

UNIFORM

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
500 
1
500 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0
ValueCountFrequency (%)
0500
50.0%
1500
50.0%
2021-02-12T12:05:55.606034image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T12:06:04.105454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Most occurring characters

ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1000
100.0%

Most frequent character per category

ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common1000
100.0%

Most frequent character per script

ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1000
100.0%

Most frequent character per block

ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Interactions

2021-02-12T11:48:32.568809image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:48:42.357330image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:48:51.730825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:49:01.067329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:49:10.326220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:49:20.738205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:49:39.974271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:49:51.770651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:50:02.749945image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:50:12.614877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:50:22.399263image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:50:31.479004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:50:40.621940image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:50:49.482515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:50:58.310669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:51:07.014068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:51:16.741608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:51:26.799251image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:51:36.827518image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:51:46.345540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:51:55.531242image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:52:04.872419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:52:14.395170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:52:24.939187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:52:34.003041image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:52:43.359622image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:52:52.070184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:53:01.162064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:53:10.310667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:53:19.382464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:53:28.663254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:53:37.743955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:53:46.314005image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:53:54.947231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:54:04.475204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:54:13.158142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:54:22.138391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:54:31.472426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:54:40.060724image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:54:49.289221image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:54:58.568817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:55:08.633707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:55:17.033619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:55:25.969331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:55:35.003181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:55:43.638582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:55:51.857988image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:56:00.439141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:56:08.686339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:56:16.834843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:56:25.454880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:56:33.963546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:56:43.142329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:56:53.238340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:57:03.555120image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:57:11.810930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:57:20.492361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:57:31.036789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:57:55.187246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:58:03.242548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:58:11.693993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:58:20.430059image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:58:29.654717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:58:38.531297image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:58:47.302284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:58:55.429247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:59:03.831181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:59:12.523557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:59:20.765778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:59:29.372282image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:59:38.395184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:59:46.911039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T11:59:55.374457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:00:05.975249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:00:14.882334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:00:23.267081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:00:31.898963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:00:40.505847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:00:49.018801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:00:57.538426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:01:05.853324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:01:14.290391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:01:22.867373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:01:32.303550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:01:41.470298image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:01:51.099472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:02:00.174011image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:02:09.158747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:02:17.487322image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:02:26.575393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-12T12:06:13.221837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-12T12:06:22.292041image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-12T12:06:30.783001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-12T12:06:39.439531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-02-12T12:02:35.733600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-12T12:02:44.435501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

X0X1X2X3X4X5X6X7X8X9target
00.0005010.5261660.582655-0.675074-0.224442-1.6207870.314257-0.199379-1.5272770.7967271
1-1.0186951.259219-2.0805380.375074-1.722629-0.259724-0.9538090.605346-2.5415661.1390901
21.310344-0.6242800.268933-0.486040-1.7214280.3290121.874681-0.094859-1.2730540.9697770
3-0.9345450.3602750.569689-1.6658830.897150-0.138467-1.8148401.832953-0.368804-1.2873281
4-0.215937-2.0758131.7002023.2750070.244070-0.0441100.0981560.7417431.8032930.1011040
5-1.000757-0.142129-0.796796-0.3992660.112551-1.4685440.889704-0.1956210.420499-0.1181660
6-0.068242-1.5321850.014384-0.150787-0.0384590.4576361.4519751.721777-0.4619100.7424090
7-0.9511830.784464-1.8763501.436871-0.418146-2.316302-1.597800-0.1197430.6842780.7977141
80.926638-1.5476160.319771-0.7256880.8553610.6672300.6131841.0512760.180791-0.1604691
9-0.2304740.2637740.284917-1.2298251.6564120.4036142.0810440.3910871.540230-0.6719690

Last rows

X0X1X2X3X4X5X6X7X8X9target
990-1.2057300.798923-1.145489-0.5573211.183418-1.8925520.490266-0.9120170.7220170.5359130
991-0.3537330.300339-0.605623-0.537616-1.231030-0.4818510.3734710.7973190.808803-0.9438271
992-0.022853-0.2317620.604073-1.720663-0.9415451.6873651.478386-2.672349-2.1276321.8145251
9931.294342-0.494163-0.244012-1.0587241.161911-0.098038-1.396900-0.5612110.4186990.1231631
9941.170055-1.0206180.459012-0.3317150.798237-0.389885-1.239365-0.6340751.054554-0.4429531
995-0.202102-0.3736600.280880-0.691766-0.4505210.068593-1.4882721.9473240.0851880.1187400
9960.0749921.537073-0.527588-0.125131-0.176318-0.470485-0.2371210.761560-0.6672400.4132640
9970.160574-0.205535-0.196561-0.023209-0.084372-1.4695901.3441590.3301590.5211811.9377850
998-0.7495690.6744200.4409220.0621160.394556-0.684705-0.245191-0.256897-2.237933-0.9738210
999-0.745437-1.0445800.3386420.612055-0.200860-0.451532-0.9474820.741946-0.6355850.3013590